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Predictors of Mortality in Patients Undergoing Autologous Stem Cell Transplant (ASCT) Admitted to the Intensive Care Unit: An Institutional Review of 1013 Transplant Patients over Five Years.

2007· article· en· W2588616464 on OpenAlexaff
Martina Trinkaus, Stephen E. Lapinsky, David Hallett, Norman Franke, Andrew Winter, Michael Crump, Donna Reece, Christine Chen, Suzanne Trudel, Ian Quirt, Armand Keating, Joseph Mıkhael

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineUnivariate analysisIntensive care unitAPACHE IISepsisMechanical ventilationMultiple myelomaRetrospective cohort studyInternal medicineSurgeryMultivariate analysis

Abstract

fetched live from OpenAlex

Abstract Study Objective: To describe the outcomes of ASCT recipients transferred to the Intensive Care Unit (ICU), and identify predictors for mortality. Methods: Retrospective review of all ASCT recipients from Jan 2001-July 2006 who required ICU transfer up to 100 days post ASCT. Measurements and main Results: Thirty-four of 1013 patients (3.3%) who underwent ASCT, were admitted to the ICU. The mean age at admission was 54.9 +/− 11.1 (range 28–71), 53% being female. Indications for ASCT included multiple myeloma (50%), amyloidosis (32%), or other malignancies (18%). Table 1 highlights the admission rate to the ICU by diagnosis. The primary admitting diagnosis in the ICU included sepsis (32%), cardiac related events (26%), or respiratory compromise (29%). Median days post ASCT was 10.0 days with a median in ICU stay of 4.0 days (range 1–37 days). Twenty patients (including all non-survivors) required mechanical ventilation for > 24 hours with a median duration of 3.0 days. Thirteen patients died (38%) in the ICU, with 11 dying of multi-system organ failure and 2 from cardiac arrest. Retrospectively collected parameters restricted to the first 24 hours of admission revealed that Sequential Organ Failure Assessment (SOFA) score (OR 1.30; CI95 1.09–1.64, P=0.003) and Acute Physiology and Chronic Health Evaluation (APACHE II) score (OR 1.43; CI95 1.14–2.16; P=0.0002) were statistically associated with mortality in univariate analysis. The variables predictive of mortality at 24 hours after admission are displayed in Table 2. Conclusion: ICU admission is uncommon, occuring in 3% of patients undergoing ASCT, of which 38% die (1% of total ASCTs). Admission is influenced by underlying diagnosis, with amyloid patients portending the highest risk. Mortality in ASCT patients admitted to the ICU can be predicted in the first 24 hours by specific assessment scores (SOFA and APACHE II); specific supportive care requirements: inotropic dependence, hemodialysis, and need for ventilation; and clinical findings of gram negative sepsis or > 2 organ failure. Patients with febrile neutropenia had a low risk of mortality (possibly due to aggressive antibiotic use, growth factors, and rapid engraftment post ASCT). These results may assist clinical decision making regarding the continuation of intensive care delivered 24 hours after admission. Percentage Admission Rate by Diagnosis (n = 1013) Diagnosis ASCT (#) ICU Admission (#)/ (%) Non-survivors (#) Multiple Myeloma 615 17 / (2.8%) 6 Non-Hodgkin’s Lymphoma 199 2/ (1.0%) 1 Hodgkin’s Lymphoma 112 1 / (0.9%) 0 Amyloidosis 39 11/ (28.2%) 6 Acute Myeloid Leukemia 17 1/ (5.9%) 0 Other (Germ Cell Tumour, Waldenstrom’s Macroglobuliemia, POEMS) 31 2/ (6.4%) 0 Variables Predictive of Mortality at 24 hours after Admission Variable Predictors Number of Patients Survivors (n = 21) Non-survivors (n = 13) P-value Febrile Neutropenia 15 13 (62%) 2 (15%) 0.013 Failure of > 2 organs 20 9 (43%) 11 (85%) 0.030 Mechanical Ventilation 20 9 (43%) 11 (85%) 0.030 Inotropic Support > 4 hours 10 3 (14%) 7 (54%) 0.022 Hemodialysis 12 4 (19%) 8 (62%) 0.025 Gram Negative Infection 6 1 (5%) 5 (42%) 0.016

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.331
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2007
Admission routes1
Has abstractyes

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